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- All Subjects: Geography
- All Subjects: spatial statistics
- Genre: Academic theses
- Creators: Myint, Soe W
This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to define urban open space is to apply fuzzy set theory to conceptualize the physical characteristics of open spaces. In addition, a 'W-green index' is developed to quantify the scope of greenness in urban open spaces. Finally, I characterize the environmental impact of open spaces' greenness on the surface temperature, explore the social benefits through observing recreation and relaxation, and identify the relationship between housing price and open space be creating a hedonic model on nearby housing to quantify the economic impact. Fuzzy open space mapping helps to investigate the landscape characteristics of existing-recognized open spaces as well as other areas that can serve as open spaces. Research findings indicated that two fuzzy open space values are effective to the variability in different land-use types and between arid and humid cities. W-Green index quantifies the greenness for various types of open spaces. Most parks in Tempe, Arizona are grass-dominant with higher W-Green index, while natural landscapes are shrub-dominant with lower index. W-Green index has the advantage to explain vegetation composition and structural characteristics in open spaces. The outputs of comprehensive analyses show that the different qualities and types of open spaces, including size, greenness, equipment (facility), and surrounding areas, have different patterns in the reduction of surface temperature and the number of physical activities. The variance in housing prices through the distance to park was, however, not clear in this research. This dissertation project provides better insight into how to describe, plan, and prioritize the functions and types of urban open spaces need for sustainable living. This project builds a comprehensive framework for analyzing urban open spaces in an arid city. This dissertation helps expand the view for urban environment and play a key role in establishing a strategy and finding decision-makings.
Evidence is presented that shows that the vegetation dynamics of the cloud forest are resilient to most of the variability in the monsoon. Much of the biodiversity in the cloud forest is dominated by a few species with high abundance and a moderate number of species at low abundance. The characteristic tree species include Anogeissus dhofarica and Commiphora spp. These species tend to dominate the forested regions of the study area. Grasslands are dominated by species associated with overgrazing (Calotropis procera and Solanum incanum). Analysis from a land cover study conducted between 1988 and 2013 shows that deforestation has occurred to approximately 8% of the study area and decreased vegetation fractions are found throughout the region. Areas around the city of Salalah, located close to the cloud forest, show widespread degradation in the 21st century based on an NDVI time series analysis. It is concluded that humans are the primary driver of environmental change. Much of this change is tied to national policies and development priorities implemented after the Dhofar War in the 1970’s.
A decreasing trend in Normalized Difference Vegetation Index over time indicates a decline in the amount of vegetation cover, which corresponds with an increasing trend in albedo and land surface temperature. Results also show a substantial difference in land cover between the control site and OHVs areas, which typically have a lower amount of vegetation cover, higher exposed sand surface, and increased anthropogenic features. Both climatic variations and OHV activity are statistically associated with land cover change in the dune field, although distinct causal mechanisms for the observed declines in vegetation cover could not be separated. The persistence of drought could inhibit vegetation growth and germination that, in turn, would hinder vegetation recovery in OHV areas. Meanwhile, repeated OHV driving has direct physical impacts on vegetation and landscape morphology, such as canopy destruction, root exposure, and increased aeolian sand transport. Active ecosystem protection and restoration is recommended to mitigate the response of declining vegetation cover and habitat loss to the impacts of OHV activity and climatic variability and allow natural recovery of re-establishement of nebkha dune ecosystems in the Algodones Dunes.
In the first part, nonlinear regression models were embedded into a multistage workflow to predict the spatial abundance of reef fish species in the Gulf of Mexico. There were two challenges, zero-inflated data and out of sample prediction. The methods and models in the workflow could effectively handle the zero-inflated sampling data without strong assumptions. Three strategies were proposed to solve the out of sample prediction problem. The results and discussions showed that the nonlinear prediction had the advantages of high accuracy, low bias and well-performed in multi-resolution.
In the second part, a two-stage spatial regression model was proposed for analyzing soil carbon stock (SOC) data. In the first stage, there was a spatial linear mixed model that captured the linear and stationary effects. In the second stage, a generalized additive model was used to explain the nonlinear and nonstationary effects. The results illustrated that the two-stage model had good interpretability in understanding the effect of covariates, meanwhile, it kept high prediction accuracy which is competitive to the popular machine learning models, like, random forest, xgboost and support vector machine.
A new nonlinear regression model, Gaussian process BART (Bayesian additive regression tree), was proposed in the third part. Combining advantages in both BART and Gaussian process, the model could capture the nonlinear effects of both observed and latent covariates. To develop the model, first, the traditional BART was generalized to accommodate correlated errors. Then, the failure of likelihood based Markov chain Monte Carlo (MCMC) in parameter estimating was discussed. Based on the idea of analysis of variation, back comparing and tuning range, were proposed to tackle this failure. Finally, effectiveness of the new model was examined by experiments on both simulation and real data.
model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that processes (relationships between the response variable and the predictor variables) all operate at the same scale. However, this posits a limitation in modeling potentially multi-scale processes which are more often seen in the real world. For example, the measured ambient temperature of a location is affected by the built environment, regional weather and global warming, all of which operate at different scales. A recent advancement to GWR termed Multiscale GWR (MGWR) removes the single bandwidth assumption and allows the bandwidths for each covariate to vary. This results in each parameter surface being allowed to have a different degree of spatial variation, reflecting variation across covariate-specific processes. In this way, MGWR has the capability to differentiate local, regional and global processes by using varying bandwidths for covariates. Additionally, bandwidths in MGWR become explicit indicators of the scale at various processes operate. The proposed dissertation covers three perspectives centering on MGWR: Computation; Inference; and Application. The first component focuses on addressing computational issues in MGWR to allow MGWR models to be calibrated more efficiently and to be applied on large datasets. The second component aims to statistically differentiate the spatial scales at which different processes operate by quantifying the uncertainty associated with each bandwidth obtained from MGWR. In the third component, an empirical study will be conducted to model the changing relationships between county-level socio-economic factors and voter preferences in the 2008-2016 United States presidential elections using MGWR.